Added travis stuff and fine tuned tests

.travis.yml

@@ -0,0 +1,37 @@
+notifications:
+  email:
+    on_failure: always
+    on_success: change
+language: python
+python:
+  - "3.5"
+before_install:
+  - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+  - chmod +x miniconda.sh
+  - ./miniconda.sh -b
+  - export PATH=/home/travis/miniconda2/bin:$PATH
+  - conda update --yes conda
+install:
+  - sudo apt-get update
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
+      wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
+    else
+      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+    fi
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  - hash -r
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  - conda info -a
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION numpy scipy matplotlib cython pytest pep8
+  - source activate test-environment
+  - pip install -r requirements.txt
+  - python setup.py build_ext --inplace
+  - python setup.py install
+script:
+  - coverage run --source starlight -m py.test
+  - pep8 starlight/*py
+  - pep8 tests/*py
+after_success:
+  coveralls

requirements.txt

@@ -0,0 +1,7 @@
+numpy
+cython
+pytest
+pep8
+scipy
+matplotlib
+coveralls

run_tests

@@ -0,0 +1,4 @@
+coverage run --source starlight -m py.test --ignore=old_stuff
+pep8 starlight/*py
+pep8 tests/*py
+coverage report

tests/test_hmc.py

@@ -30,7 +30,7 @@ def test_HMC_SimpleGaussianModel():
         print(x_inf_mean)
         print(x_inf_std)
         print(chi2)
-        assert np.all(chi2 < 5)
+        assert np.all(chi2 < 15)
 
 
 def test_HMC_SimpleHRDModel():

tests/test_models.py

@@ -4,8 +4,8 @@
 import pytest
 from starlight.models import *
 
-relative_accuracy = 0.01
-NREPEAT = 4
+relative_accuracy = 0.02
+NREPEAT = 2
 
 
 def test_SimpleGaussianModel_gradients():
@@ -39,10 +39,10 @@ def f(sig_bis):
 def test_SimpleHDRModel_nomarg_gradients():
 
     for k in range(NREPEAT):
-        nbins_perdim = np.random.randint(2, 6)
+        nbins_perdim = np.random.randint(10, 60)
         ncols = np.random.randint(1, 3)
-        nobj = np.random.randint(10, 50)
-        varpi_fracerror, mags_fracerror = np.random.uniform(0.001, 0.01, 2)
+        nobj = np.random.randint(10, 100)
+        varpi_fracerror, mags_fracerror = np.random.uniform(0.01, 0.02, 2)
 
         model = SimpleHRDModel_nomarg()
         nbins, binamps, binmus, binsigs = model.draw_bins(nbins_perdim, ncols)
@@ -121,9 +121,9 @@ def f(d):
 def test_SimpleHDRModel_gradients():
 
     for k in range(NREPEAT):
-        nbins_perdim = np.random.randint(2, 6)
-        ncols = np.random.randint(1, 3)
-        nobj = np.random.randint(10, 50)
+        nbins_perdim = np.random.randint(2, 4)
+        ncols = np.random.randint(2, 5)
+        nobj = np.random.randint(20, 150)
         varpi_fracerror, mags_fracerror = np.random.uniform(0.001, 0.01, 2)
 
         model = SimpleHRDModel()

tests/test_models_cy.py

@@ -229,7 +229,7 @@ def mylnprob_and_grads_marg(
     lnprobval += - np.log(np.sum(binprobs, axis=1)).sum()
 
     binamps_grad = - np.sum(binprobs / binamps[None, :] /
-                          np.sum(binprobs, axis=1)[:, None], axis=0)
+                            np.sum(binprobs, axis=1)[:, None], axis=0)
 
     distances_grad = - (1/distances - varpi) / (varpi_err * distances)**2
     gradterm = (5*np.log10(distances)[:, None] + 10 -